elasticsearch-7.17.6及对应版本IK分词

elasticsearch-7.17.6及对应版本IK分词 适合人群：elasticsearch初学者 Elasticsearch 是位于 Elastic Stack 核心的分布式搜索和分析引擎。Logstash 和 Beats 有助于收集、聚合和丰富您的数据并将其存储在 Elasticsearch 中。Kibana 使您能够以交互方式探索、可视化和分享对数据的见解，并管理和监控堆栈。 Elasticsearch 为所有类型的数据提供近乎实时的搜索和分析。无论您拥有结构化或非结构化文本、数字数据还是地理空间数据，Elasticsearch 都能以支持快速搜索的方式高效地存储和索引它。您可以超越简单的数据检索和聚合信息来发现数据中的趋势和模式。随着您的数据和查询量的增长，Elasticsearch 的分布式特性使您的部署能够随之无缝增长

粒子群算法（PSO）解决柔性作业车间调度问题（附标准测试数据及优解）

之前与老师做项目的时候写的粒子群算法解决柔性作业车间调度问题，是用Java写的，标准测试数据及优解在data文件夹下

JFLAP7.1.jar

FLAP最新版，双击.jar包即可运行。可以画有限自动机（NFA，DFA，miniDFA），也可以画图灵机等，对于需要的人来说，这是一个非常好用的工具。

C++遗传算法解决柔性作业车间调度（附测试数据及优解）

用c++写的遗传算法解决柔性作业车间调度问题，主要参考论文 张国辉, 高亮, 李培根, et al. 改进遗传算法求解柔性作业车间调度问题[J]. 机械工程学报, 2009, 45(7):145-151.

蜂群算法解决柔性作业车间调度Java（附测试数据）

人工蜂群算法解决柔性作业车间调度问题，用Java写的。参考论文：陈少, 吉卫喜, 仇永涛, et al. 改进人工蜂群算法求解柔性作业车间调度问题[J]. 组合机床与自动化加工技术, 2018, No.531(05):166-169.

遗传算法解决旅行商问题（附标准测试数据）

传统遗传算法解决旅行商问题，附带标准测试用例及样例的已知最优解，算法结果与已知最优解相差不大

cec 2013 代码及论文

Single objective optimization algorithms are the basis of the more complex optimization algorithms such as multi-objective optimizations algorithms, niching algorithms, constrained optimization algorithms and so on. Research on the single objective optimization algorithms influence the development of these optimization branches mentioned above. In the recent years various kinds of novel optimization algorithms have been proposed to solve real-parameter optimization problems. Eight years have passed since the CEC’05 Special Session on Real-Parameter Optimization[1]. Considering the comments on the CEC’05 test suite received by us, we propose to organize a new competition on real parameter single objective optimization. In the CEC’13 test suite, the previously proposed composition functions[2] are improved and additional test functions are included. This special session is devoted to the approaches, algorithms and techniques for solving real parameter single objective optimization without making use of the exact equations of the test functions. We encourage all researchers to test their algorithms on the CEC’13 test suite which includes 28 benchmark functions. The participants are required to send the final results in the format specified in the technical report to the organizers. The organizers will present an overall analysis and comparison based on these results. We will also use statistical tests on convergence performance to compare algorithms that eventually generate similar final solutions. Papers on novel concepts that help us in understanding problem characteristics are also welcome.